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Fig. 3. A clinical decision tree to predict a bacteremic patient’s likelihood of infection with an extended-spectrum β-lactamase (ESBL)–producing organism at the time of organism genus and species identification, adapted from Goodman et al (2016).8 Gray-shaded terminal nodes indicate that the tree would classify patients as ESBL positive, and accompanying percentages (derived from terminal-node impurities) reflect the probability that patients assigned to a given terminal node are ESBL-positive. Terminal node numbering (1–6) is included in parentheses. *Latin America (excluding the Caribbean), the Middle East (includ- ing Egypt), South Asia, China, and the Mediterranean.
predictor evaluated).18,19 In contrast, CART is nonparametric and makes fewer data assumptions,13 and it can accommodate collinear independent variables. It is also less sensitive to outliers and more robust to high-dimensional data, which possess many independent variables relative to outcomes. These features are appealing in MDRGN research, given the abundance of predictors in patient medical records but the relative rarity of clinical outcomes. Moreover, logistic regression requires a priori specification and evaluation of variable interactions, whereas CART identifies inter- actions without user input,13 a potentially helpful feature when the understanding of variable relationships is generally limited. The benefits of CART, however, can come with a steep learning
curve for researchers without prior experience with these methods. In particular, decision trees are prone to overfitting, in which they fit the data “too well” (including its idiosyncrasies and noise) and may consequently perform poorly on new data.20 Sufficient exper- tise in pruning and/or stopping criteria during the tree-branching process is therefore critical to the utility and generalizability of the resulting tree, as is the use of internal validation methods (eg, cross-validation) when external testing datasets are unavail- able. Although ensemble tree methods such as random forests analysis can address many of these challenges, these methods do not produce a single decision tree that can be used as a decision support tool (without automation).21,22 Decision tree branching logic does not require calculations, and
decision trees are generally intuitive and user-friendly. When manual bedside use is anticipated, these features are especially ben- eficial. As facilities incorporate automated decision support tools and algorithms into electronic health records (EHRs), these bene- fits attenuate. In this ESBL case study, because important variables
required clinical judgment (eg, source of infection) or were not hard-coded in the EHR (eg, foreign country of recent hospitali- zation was only entered as natural language), automating the deci- sion support tool would have been challenging. As a result, the decision tree’s simplicity for manual bedside use was highly valu- able for this research application. Finally, for applications in which decision support tool flexibil-
ity is paramount, risk scores are attractive because their cutoff points are modifiable by end users. Risk scores provide a range of score cutoffs, each with an associated sensitivity and specificity, which allow individual users to toggle the cutoff point to minimize the false-positive or false-negative rate (eg, depending upon infec- tion severity or the clinical appearance of the patient). Using the current risk score, for example, a user seeking to increase sensitivity could choose a lower cutoff point of ≥3 points and reduce the risk of incorrectly classifying an ESBL infection as ESBL negative to<1 in 5 (sensitivity 83.5%, specificity 73.1%) (Table 1). This flex- ibility allows clinicians and hospital epidemiologists to maximize detection of cases (ie, ESBL-positive patients), though at the cost of attendant reductions in specificity and overall classification accuracy. We caution, however, that although enhanced flexibility is gen-
erally beneficial, a risk score’s utility depends upon users under- standing the score and the implications of adjusting the cutoff point. Large score differences between patients may translate to minimal differences in risk, and vice versa. Moreover, cutoff-point positive and negative predictive values (ie, the probability that a patient does or does not have an ESBL-producing infection given a score that is respectively above or below the selected cutoff point) will vary by ESBL prevalence in the target population. It is
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